Introduction

This lab is a (brief) introduction to Conservation Planning in R, and more generally an introduction to a few ways to visualize and analyze spatial data in R. The key word is introduction: this exercise should give you a taste of what I think is a very powerful and flexible software that can potentially have many applications for Conservation Planning.

First things first

Make sure you have downloaded and installed the latest versions of R and Rstudio.

Why R?

Lots of digital ink has been spilled explaining the advantages (and limitations) of R. For the purposes of this class, I would say that R has a few advantages that make it potentially useful for Conservation Planning:

  1. It is completely open source, meaning that anyone can download and use it (for free), and anyone can contribute to it. It is a living language, meaning that it is constantly being updated and improved, and is only growing in popularity. Additionally, R’s open source nature means that there are a lot of practitioners willing to help each other out with code and questions. It is an active community.
  2. R allows you to create transparent, fully reproducible analyses, facilitating knowledge-sharing. For example, I can share this code with you, and it should (likely) run just the same for you as it does for me, allowing you to see not just my results, but exactly how I got there
  3. Going along with the above, R empowers you to write scripts and functions that be run and tweaked multiple times. No more remembering which buttons to push in ArcGIS, or writing out a workflow for MaxEnt by hand so you don’t forget: If you have a functional piece of R code, and decide later that you want to tweak some parameter, or add a few steps, this is easily done if your analysis is in R.
  4. Many conservation planning tools are now implemented in R. This includes tools you are familiar with, including MaxEnt (in the ‘dismo’ package, which stands for distribution modeling), Marxan, Circuitscape, and other newer tools like Prioritzr, that is similar to Marxan but is fully optimized and much faster.

Right off the bat, I want to point you to some interesting and useful CP and R resources:

  • Chris Brown, a researcher at the Australian Rivers Institute, has a blog with some great examples of R code used in conservation planning purposes
  • The National Center for Ecological Analysis and Synthesis, or NCEAS, right here in our own backyard, always has a number of existing, data-driven projects going, and many of their scientists and staff work in R on spatial analysis and/or conservation planning. You can find many of their helpful R tutorials here. This lab is a direct adaptation of an NCEAS tutorial by Jamie Afflerbach.
  • Googling! As an open source platform with hundreds of thousands of practitioners, R has a large and active community. Googling around can be a great way to get answers to your questions
  • Many R packages (the cohesive collections of functions that can be written by anyone, and form the guts of R’s functionality) come with extensive help files, tutorials, and examples, sometimes called vignette. If you want to know what a package or function does, typing ? followed by the function or package name (e.g. ?raster) is always a good place to start!

Research Questions

We will be utilizing some data from our previous lab on hotspots in the California Current to produce some threat layers of interest. This will be a two part lab. In the first, we will look at compiling different categories of threats, like the authors did in Halpern et al. (2009). The guiding research questions are:

1. What are the spatial mismatches of local vs. global threats in the CA Current?

2. (for week 2 of the lab) Where were MPAs placed? Which threats do they address?

Giant sea bass Stereolepis gigas, a critically endangered species in southern California

Giant sea bass Stereolepis gigas, a critically endangered species in southern California

Part 1: Setup

In the first part of the lab, we’ll be working with raster data. As a reminder, raster or gridded data are stored as a grid of values which are rendered on a map as pixels.

Examples

Some examples of raster data include oceanographic datasets such as Sea Surface Temperature, land use maps and digital elevation maps, and of course, our California Current threat data.

See a great intro to raster data in R here

GeoTIFFs

Raster data can come in many different formats. In this lab, we us geotiff data which has the extension .tif. A .tif file stores metadata or attributes about the file as embedded tif tags. These tags can include the following raster metadata:

  1. A Coordinate Reference System (CRS)
  2. Spatial Extent (extent)
  3. Values that represent missing data (NoDataValue)
  4. The resolution of the data

Libraries & Settings

There are a lot of spatial packages for R, we will touch on some of them here but not all of them. Here is brief overview, taken from this site:

  • raster: Reading, writing, manipulating, analyzing and modeling of gridded spatial data
  • rgdal: Provides the most important and basic spatial functionalities. Provides bindings to Frank Warmerdam’s Geospatial Data Abstraction Library (GDAL) (>= 1.6.3, < 2) and access to projection/transformation operations from the PROJ.4 library
  • sp: provides classes and functions for spatial data
  • rgeos: Provides spatial vector operations like buffer and intersect. Interface to Geometry Engine – Open Source (GEOS) using the C API for topology operations on geometries.
  • maps: This package has pre-loaded maps stored which can be added to your map plots.
  • maptools: tools for reading and writing spatial data (visualisation)
# if need be, you can install the packages you don't have with the command install.packages(), 
# with the package names in quotes:
# install.packages("raster","rgdal","rasterVis","maps","rgeos","dplyr","RColorBrewer")

# Load the libraries into this R session
library(raster)       #Main raster library with nearly all functions used in this analysis
library(rgdal)        #Spatial library - most functions used from rgdal are for vectors (shapefiles)
library(rasterVis)    #Useful for raster visualizations
library(maps)         #Has a database of maps. I use this to add a map to my raster to visualize land boundaries
library(rgeos)        #Need this library for topology operations on geometries
library(dplyr)        #NOT spatial - this is a data wrangling library
library(RColorBrewer) #Also not spatial - used to set the spectral color scheme

Now, we should be ready to actually begin looking at data!

Part 2: Importing Raster Data

For the first part of this lab, we will re-do the hotspot analysis from Week 3, to show how analyses in R are directly comparable to (and in my opinion, easier!) those done in other programs like ArcGIS.

Colors are important for digestible visualization in R, just like in ArcMap or any other software. I don’t particularly like the default color scheme in R, so here we set up our own.

# rainbow color scheme
cols = rev(colorRampPalette(brewer.pal(11, 'Spectral'))(255)) 

#setting smaller margins for plotting
par(mar=c(2,2,1,1))

Read in Raster Data

Let’s import the raster with the cumulative threat layer. Create a raster of the first file by calling raster() (think of this function like read.csv() for raster data, if you are familiar with that function).

all_threats <- raster("E:/TA files/CP2017_Owen/R lab development/Threats_data/full_modelnv.tif")

Visualize

We can then use plot() to plot the raster; it’s that easy! To override the default color scheme, we define the argument col= as our own scheme cols from above.

plot(all_threats,col=cols)

We can add a basemap to our raster by utilizing the maps package

# add a landmap to your shapefile. the add=T argument tells R to add it to the existing plot.
# make sure you understand what the other arguments do
plot(all_threats,ext=extent(-130,-110,24,50),col=cols)
map('world',fill=T,add=T,col='gray')

Extent and Zoom

Visualizing exploring rasters in different ways can give us a great idea of our data, without even running much analysis. You can visualize a different extent, or subset, of the data:

plot(all_threats,col=cols,ext=extent(-121,-117,32,35),main="Cumulative Threats") # A good extent for the Santa Barbara Channel

The zoom() function, by default, allows you to draw your own extent just by clicking twice.

plot(all_threats,col=cols)

# zoom(all_threats,col=cols) #Interactive code not run in html

QUESTION: Which part of the SF Bay area is at highest threat?

Raster data attributes

Beyond visualization, we can also look at some simple characteristics of the data itself. Just calling the name of our raster will give us some information:

all_threats
## class       : RasterLayer 
## dimensions  : 3659, 4407, 16125213  (nrow, ncol, ncell)
## resolution  : 0.009448675, 0.009448675  (x, y)
## extent      : -138.7553, -97.11496, 21.61105, 56.18375  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
## data source : E:\TA files\CP2017_Owen\R lab development\Threats_data\full_modelnv.tif 
## names       : full_modelnv 
## values      : 1.027101, 83.84592  (min, max)

QUESTION: What is the minimum value of all raster cells in our threats layer?

We can look at the frequency histogram of raster cell values.

hist(all_threats,main="Cumulative Threats Frequency")

Also, the function cellStats() allows us to run some basic statistics. Type ?cellStats to read what the function can do

cellStats(all_threats,mean)
## [1] 14.00092

QUESTION: What is the standard deviation of all raster cells in our threats layer?

Part 3: Raster Calculations

Quickly visualizing raster data in R is nice, but the real power of raster analysis is when we can perform calculations that link two or more raster layers. If you remember from our hotspots lab, our first task was to overlay the top 20% of cumulative threats with the top 20% of species richness, to find threat hotspots. This will require 4 steps:

  1. Import the data (already done for threats)
  2. Resample the data to the same resolution
  3. Reclassify the data to find the top 20%
  4. Overlay the data and find hotspots

Import Species Data

The species data is in the Species data directory, and we import it just as we did the threats layer, by providing a full path name. We can then check its attributes.

all_spp <- raster("E:/TA files/CP2017_Owen/R lab development/Species_data/ca_curr_sp_rich.tif")
all_spp
plot(all_spp,col=cols)

Cropping and Resolving

If you type all_spp and all_threats (or plot them), you should be able to tell that we may run into problems trying to immediately do calculations on them, as they have different extents and resolutions. Two helpful functions to deal with these problems are crop() and resample().

QUESTION: Before doing the next step, which of our two rasters has a greater extent? a higher (finer) resolution? What does this mean about how we should resample?

Crop

We first crop the threats layer to the extent of the species layer

#?crop see what the crop function does

threats_crop <- crop(all_threats,all_spp) #Crop the threats layer to the same extent at species

Resample

Now the threats layer has the same extent as the species layer. But we have to resample the species layer such that it has the same resolution as the threats layer.

#?resample see what the resample function does
# NOTE: the progress='text' argument is a great tool: it prints out the progress
# of a longer-running function into the console, so you can see how the operation is going

# the method='ngb' argument specifies that we want to use a nearest neighbor algorithm to resample, instead of interpolation
spp_res <- resample(all_spp,threats_crop,method='ngb',progress='text')
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |================                                                 |  25%
  |                                                                       
  |================================                                 |  50%
  |                                                                       
  |=================================================                |  75%
  |                                                                       
  |=================================================================| 100%
##

Raster Stack

We can check that the two layers will line up decently by using the stack() function. stack() creates a RasterStack object, which is basically just exactly what it sounds like: A single object holding multiple raster layers. This isn’t as useful for just a couple of layers, but it will likely be useful to you later on when you are combining multiple threat layers.

spp_threat_stack <- stack(threats_crop,spp_res)
plot(spp_threat_stack,col=cols)

Even though this layers still look similar to how they did before, the fact that they stacked together means they are likely ready for combination.

Reclassify

Remember, we want to find the top 20% of cells in each layer, and then combine the two layers to produce hotspots. We do this with the reclassify() function from the raster package. Type ?reclassify and make sure you understand what the function does, especially the rcl argument.

First, let’s focus on the species layer.

hist(spp_res,main="Species Raster Values")

Re-assign Zeroes

This layer has a huge number of zeroes, which, from the graph seem like they should be NAs. We can reassign zero values to NA by calling the reclassify function.

# notice that in the following, we are OVERWRITING the original spp_res object.
# This is okay in this instance since we won't be using the old version, but
# often it is better to assign any output of a function to a new variable or object
spp_res <- reclassify(spp_res,rcl=c(-Inf,0,NA))
hist(spp_res,main="Species Raster Values, Zeroes Removed") # did the function do what we were hoping?

Reclassify Species

Next, we find the top 20% of the species data, assigning those cells a value of 1, and all the other non-NA cells a value of zero. The quantile() function is very useful for this.

#?quantile what does the quantile function do?
spp_cutoff <- quantile(spp_res,0.8) # Find the value of the 80th percentile
spp_maxVal <- cellStats(spp_res,max) #find the maximum

# Our reclassification matrix. Make sure you know what this is saying
rcl_mat <- c(-Inf,spp_cutoff,0,
            spp_cutoff,spp_maxVal,1)

# Reclassify the species layer
spp_binary <- reclassify(spp_res,rcl=rcl_mat)

We can visualize the result of this reclassification just by plotting, as before:

# Because we have binary data now, I want to change the color scheme again
binary_cols <- c("white","firebrick")
plot(spp_binary,col=binary_cols,legend=F,main="Top 20% of Species Richness")
map('world',fill=T,add=T,col='gray')

***

Reclassify Threats

Now we just do a similar process for the threats.

TASK: Reclassify the threats layer (make sure it is the one with the correct extent!) to assign a value of 1 to the top 20% of the cells, and a zero to the others

It should look something like this:

Hotspots

Now, all we have to do is overlay the two layers to find our hotspots. We do this with the overlay() function, which takes two or more rasters and applies a function to them, given by us in the fun argument. In this case, because the two rasters we are combining are just 1s and zeroes, we can just add them together! But remember for later that you can write your own function to combine rasters in other ways.

# the hotspots
hotspots <- overlay(spp_binary,threat_binary,fun=function(x,y){x+y})

# color breakpoints. We need three colors now! (cell values of 0,1,or 2)
brks_hotspots <- seq(0,3,length.out=4) 
hotspot_cols <- c("white","lightblue","firebrick") #

# plot the hotspots!
plot(hotspots,col=hotspot_cols,legend=F,main="Hotspots");map('world',fill=T,add=T,col='gray80')

plot(hotspots,col=hotspot_cols,ext=extent(-121,-117,32,35),main="Hotspots, SB Channel",legend=F)
map('world',fill=T,add=T,col='gray80')

Voila! Our hotspots.

All of the Above

This may have seemed like a lot to you. You may have been thinking “R seems much more complicated than just doing this in ArcMap”. You are of course entitled to your opinion, but once you are a little bit familiar with R, I personally believe you will find it much easier and quicker than a lot of the clunkiness that comes along with ArcGIS. For example, here is all of the above code to make hotspots in less than 30 lines of code. I would argue this is MUCH faster than doing this analysis in Arc. And it is fully reproducible, (usually) throws understandable error messages if something goes wrong, and is easy to share with others and adapt to other data or purposes.

Notice that I wrote and included my own function (in this case, a function that takes a raster and a given quantile, and returns a binary raster, assigning ones and zero to those cells above or below a threshold quantile). Writing your own functions is SUPER useful in R, and if you are interested, try writing your own for the next part of this lab.

### import data ###
all_spp <- raster("E:/TA files/CP2017_Owen/R lab development/Species_data/ca_curr_sp_rich.tif")
all_threats <- raster("E:/TA files/CP2017_Owen/R lab development/Threats_data/full_modelnv.tif")

#### Crop, resample, and reclassify ###
all_spp <- reclassify(all_spp,rcl=c(-Inf,0,NA)) # reclass 0 to NA
threats_crop <- crop(all_threats,all_spp) # crop threats to species
spp_res <- resample(all_spp,threats_crop,method='ngb') # resample species to threat's resolution

#### Function to output a binary raster based on a user-given quantile (default is top 20%) ###
reclassify_topx <- function(rast,quant=0.8) {
  topx <- quantile(rast,quant) #find the 80% quantile of the raster values
  maxVal <- cellStats(rast,max) #find the maximum
  rcl <- c(-Inf,topx,0,
            topx,maxVal,1) # reclassify matrix (see help file for ?reclassify)
  out <- reclassify(rast,rcl=rcl)
  return(out) # returns the new binary raster
}

### Find top 20%, using the code from above. We could easily choose a different quantile here. ###
all_threats_top20 <- reclassify_topx(threats_crop,quant=0.8)
all_spp_top20 <- reclassify_topx(spp_res,quant=0.8)

### overlay and plot the hotspots ###
hotspots <- overlay(all_threats_top20,all_spp_top20,fun=function(x,y){x+y})
brks_hotspots <- seq(0,3,length.out=4) # color breakpoints
hotspot_cols <- c("white","lightblue","firebrick") # colors
plot(hotspots,col=hotspot_cols);map('world',fill=T,add=T,col='gray80')

Your Turn

The above showed you some of the basics of raster analysis in R. Now it’s your turn to apply these tools to a related spatial analysis problem. If you remember, our guiding question for this week was:

What are the spatial mismatches of local vs. global threats in the CA Current?

Using the table below, choose a set of threat layers (Climate, Fishing, Land-Based, or Other), and:

  1. Download the appropriate set of rasters (see Table) and load them into R.

  2. Stack the layers and plot them (HINT: if you use the stack function, then you can just call plot once and it will plot all of the layers)

  3. Get an idea of the distribution of the data using hist(), cellStats(), or visually by zooming in to different sections of your rasters.

  4. Overlay the rasters and perform raster calculations (reclassify, overlay, maybe calc) to create a cumulative threat layer. You may do this however you deem appropriate. Some suggestions:

    • A quantile and sum approach, analagous to the above analysis
    • A mean threat value per raster cell
    • A maximum threat value per raster cell
    • A weighted mean

There are many ways to perform these analyses. It may be helpful to outline your analysis by hand or with commented lines (##) before you begin, so you have a clear idea of the steps involved. If you run into errors with certain functions, make sure to read the help files first (e.g., ?overlay), which may give you an idea of what went wrong.

File Name Description Threat Category
impact_acid Climate change: ocean acidification Climate
impact_uv Climate change: sea temp. change Climate
impact_sst Climate change: UV change Climate
impact_rec_fish Fishing: recreational Fishing
impact_dem_nd_hb Fishing: demrs. non-des. high bycatch Fishing
impact_dem_nd_lb Fishing: demrs. non-des. low bycatch Fishing
impact_pel_hb Fishing: pelagic high bycatch Fishing
impact_pel_lb Fishing: pelagic low bycatch Fishing
impact_dem_d Fishing: demersal destructive Fishing
impact_nutrients Nutrient input Land-Based
impact_sed_decrease Sediment input: decrease Land-Based
impact_sed_increase Sediment input: increase Land-Based
impact_organic Pollution input: organic Land-Based
impact_inorganic Pollution input: inorganic Land-Based
impact_coastal_e Coastal engineer.: habitat alteration Land-Based
impact_beach_a Direct human impact: beach trampling Land-Based
impact_light_pol Pollution input: light/noise Land-Based
impact_pplants Power, desalination plants Land-Based
impact_dep_ocean Pollution input: atmospheric Land-Based
impact_trash Ocean dumping: marine debris Other
impact_pollution Ocean pollution (from ships/ports) Other
impact_shipping Shipping (commercial, cruise, ferry) Other
impact_pens Aquaculture: finfish (predators) Other
impact_invasives Invasive species (from ballast, etc.) Other
impact_oil Benthic structures (e.g. oil rigs) Other